Health care fraud continues to be a growing problem in the United States and abroad. According to the Center for Medicare and Medicaid Services (CMS—formerly the Health Care Finance Administration, or HCFA) “fraud is the intentional deception or misrepresentation that an individual knows to be false or does not believe to be true and makes, knowing that the deception could result in some unauthorized benefit to himself/herself or some other person.” The CMS states that the most frequent kind of fraud arises from a false statement or misrepresentation made, or caused to be made, that is material to entitlement or payment under the Medicare program. The violator may be a physician or other practitioner, a hospital or other institutional provider, a clinical laboratory or other supplier, an employee of any provider, a billing service, a beneficiary, a Medicare carrier employee or any person in a position to file a claim for Medicare benefits.
Fraud schemes range from those perpetrated by individuals acting alone to broad-based activities by institutions or groups of individuals, sometimes employing sophisticated telemarketing and other promotional techniques to lure consumers into serving as the unwitting tools in the schemes. Seldom do perpetrators target only one sector, public or private, exclusively. Seldom do perpetrators limit fraud schemes to one insurer. Rather, most are found to be defrauding several private and public sector victims, such as Medicare, simultaneously.
According to a 1993 survey by the Health Insurance Association of America of private insurers' health care fraud investigations, overall health care fraud activity broke down as follows:                43% Fraudulent diagnosis        34% Billing for services not rendered        21% Waiver of patient deductibles and co-payments        2% Other        In Medicare, the most common forms of fraud includes:        Billing for services not rendered or medically unnecessary        Providing services more often than is medically necessary by Medicare standards, i.e. overutilization        Misrepresenting the diagnosis to justify payment        Soliciting, offering, or receiving a kickback        Unbundling or “exploding” charges        Falsifying certificates of medical necessity, plans of treatment, and medical records to justify payment        Billing for a service not furnished as billed; i.e., upcoding the level of care for higher reimbursement, or routinely provide a higher level of care than is the general standard        Performing “gang visits”, e.g. a single visit made to treat a number of people in a nursing home that's billed as multiple individual visits        Referring patients to another provider for services that aren't medically necessary, i.e. “ping-ponging” (typically screening procedures are at fault)        Transferring (“dumping”) an uninsured patient (or insured with low reimbursement rates) from one emergency room to another facility in violation of the patient anti-dumping statute.        
According to the Center for Medicare and Medicaid Services annual health care expenditures in the United States total nearly $1.1 trillion. The nation's actual 1997 expenditure, for example, totaled $1,092.4 billion. The amount lost to health care fraud and abuse can never be quantified to the dollar. Although estimates of the losses vary widely, a general range may be obtained from literature. For example, in May 1992, citing health insurance industry sources, the US General Accounting Office (GAO) reported to Congress that the loss amounted to as much as 10% of the nation's total annual health care expenditure—or as much as $84 billion in 1992 alone. This high-end estimate of 10% remained common in 2000, at a time when annual US health care spending totaled more than $1 trillion. Many private insurers, for example, when asked their estimates of the proportion of health care dollars lost to fraud, responded with a loss figure ranging from 3-5%, which amounts to roughly $30-$50 billion, annually. In July 1997, based on the first comprehensive-audit of Medicare claims paid, the Inspector General of the U.S. Department of Health and Human Services reported to Congress that approximately 14% of Medicare claims dollars—representing some $23 billion—were paid inappropriately, due to fraud, abuse, and lack of medical documentation to support claims. It is widely accepted that losses due to fraud and abuse are an enormous drain on both our public and private healthcare systems.
One of the ways in which fraud can be evaluated, both in the medical care setting and more generally, in any setting involving a transactional relationship is by modeling interactions between different entities such as individuals, organizations or groups. In such cases, the activity related to the problem at hand is largely described by a body of transaction data (historical and/or ongoing) that captures the behaviors of the relevant entities. A few sample settings along with the corresponding transaction data and related entities are described below in Table 1.
TABLE 1Problem/SettingTransactionsEntitiesHealthcare fraudClaims (inpatientClient (Patient),and abuse detectionand outpatient)Doctor, Hospital,Pharmacy, LabCredit CardPurchases,Account holder,fraud detectionPayments, Non-Merchant, Credit Cardmonetary transactionsissuerBank CheckingCheckAccount holder,SystemprocessingBank, TellertransactionsFood StampFood StampRetailer, Clientfraud detectiontransactions
In each of these settings, the common phenomenon is the fact that the encounters between the different entities are captured in the form of the associated transactions.
An entity is an operational unit within a given setting, application or environment and represents objects that interact within that setting, application or environment. The members of an entity are generally objects of a similar type. Different entities interact with each other and their interactions are encapsulated in the transaction data corresponding to that application. Thus, examples of entities in a healthcare setting are clients, providers (this includes doctors, hospitals, pharmacies, etc.), clients' families, etc. and their interactions are captured in the claims data; i.e. the interaction of a healthcare provider and a patient is captured in a claim by the provider for reimbursement. In the credit card world, the interacting entities are account holders, merchants, credit card issuers, and the like and their interactions are captured through different types of transactions such as purchases and payments.
Usually, entities correspond to individuals or organizations that are part of the setting, as the examples in the previous paragraph illustrate. However, more abstract entities characterizing a transaction may also be defined. Examples include procedure codes (describing the type of healthcare service rendered), resource utilization groups (RUG's), diagnosis-related groups (DRG's), and SIC codes (Standard Industry Codes), etc.
The member of an entity is an individual instance of the entity. For example, a specific doctor is a member of the healthcare provider entity; a particular grocery store is a member of the credit card merchant entity and so on.
A target entity is the primary entity of interest for a given application. Usually, it is the focus of some type of analysis such as a statistical model or a rule. A target entity interacts with other entities through the transactions. Thus, in provider fraud and abuse detection, the healthcare providers are the target entity while the clients (patients), clients' families, other providers, etc are the entities interacting with the target entity. In credit card fraud, the merchant would be one example of a target entity (depending upon the type of fraud being analyzed) and the interacting entities then are the cardholder, the credit card issuer, etc. Alternatively, a point of sale terminal could be another type of target entity, and the cashiers who use the terminal would be the interacting entities.
As noted above, a transaction captures the information associated with an interaction between a group of entities. A transaction may initially arise between two entities (e.g. a doctor and a patient) and then be processed by still other entities (e.g. a pharmacy providing a prescription and a laboratory providing a lab test required by the doctor). Different types of transactions will typically capture different types of interactions or interactions between different groups of entities. For example in the credit card setting, a purchase transaction captures the interaction between the cardholder and the merchant, while a payment transaction encapsulates the information regarding the payments made by a cardholder to the credit card issuer. Similarly, in healthcare, an outpatient claim represents the service received by a client (i.e. patient) from a provider as part of an office or home visit, while an inpatient claim encodes data regarding a patient's stay at a hospital or another facility.
In the past, profiles have been created for individual entities and used to develop statistical models based solely on the profiles of the individual entities. For example, U.S. Pat. No. 5,819,226 discloses, among other things, the use of profiles of individual credit card account holders for modeling credit card fraud by such individuals. While this approach is useful for particular applications, n other applications it is desirable to understand the complex interactions between different entities. For example, in order to determine whether there is fraudulent activity by a health care provider, it is important to view the provider's activity not just in a vacuum, but also in relation to the activities of all other providers. Accordingly, profiles based only on transactions of individual members of the entity are insufficient to capture these rich interactions between entities in a manner that yields statistically useful information for modeling the interactions between entities. The ensuing section gives a brief summary of the invention along with the specifics on how the invention captures these interactions between entities.